Static Fatigue Detection in Office Syndrome using sEMG and Machine Learning

Office syndrome is one of the important health issues worldwide. Sitting in one position for an extended period of time causes muscle fatigue. This study proposed static fatigue detection in office syndrome using surface electromyography (sEMG) and machine learning. The sEMG was recorded by EMG sensor board connected with NodeMCU V2 ESP8266 during sitting position with surface electrodes on the shoulder. The signals were extracted and preprocessed to obtain different features of datasets. Six machine learning models (Logistic Regression, Support Vector Machine, Naive Bayes, k-nearest Neighbors, Decision Tree, and Multi-layer Perceptron) with seven features (mean, integrated EMG, mean absolute value, mean absolute value1, mean absolute value2, simple square integral, and root mean square) of original datasets and featured-selected data were trained and tested, predicting an output class of fatigue or non-fatigue. Featured-selected data in this research were categorized to feature set I (mean, integrated EMG, mean absolute value, simple square integral, and root mean square) and feature set II (integrated EMG, mean absolute value2, and simple square integral). Consequently, multi-layer perceptron on feature set II has the best accuracy at 99.6690 percent with fit time of 18.322849 seconds. However, considered on the accuracy of 99.2482 percent and the fit time of 0.027955 seconds, decision tree could be an alternate machine learning model in this study.